The worksheet HOUSE contains data on houses sold in Stockton, CA, during 1996-19
ID: 3365670 • Letter: T
Question
The worksheet HOUSE contains data on houses sold in Stockton, CA, during 1996-1998. Estimate the following model. PRICE = + SQFT + AGE + BEDS + BATHS + u. Build a 95% interval estimate for the price difference between a 2-year house and a 10-year old house.
PRICE SQFT AGE BEDS BATHS 138000 1700 97 3 2 105700 2100 18 4 2.5 22000 700 49 2 1 255000 3000 23 3 3 203000 2100 18 4 2 129178 1600 2 3 2 140000 1700 0 3 2 118600 1400 4 3 2 125000 1400 3 3 2 145000 1700 1 3 2.5 157000 1700 3 3 2.5 160000 1900 4 4 2.5 151000 1700 0 3 2.5 166000 1900 0 4 2 124500 1400 1 3 2 145000 1400 3 3 2 144000 1600 0 4 2 175000 1800 3 4 2 125000 1400 0 3 2 172500 2200 2 4 2.5 330000 3500 0 4 3.5 233900 2400 0 4 2 199950 2100 0 3 3 159000 1900 7 4 2 180000 2600 5 4 3 155000 2300 6 4 2.5 129000 1600 6 3 2 149900 2700 6 4 3 235000 3300 24 5 3.5 137000 1700 29 3 2 219000 2100 23 3 2 210000 2100 22 3 3 190000 2200 22 2 2.5 247000 2500 24 4 3 125000 1600 3 3 2 199000 2600 4 4 3 132500 1700 4 4 2 161000 2100 2 4 2.5 166300 2100 4 4 2.5 146500 2300 1 4 3 180000 2600 3 4 3 142000 1900 4 3 2 149950 1800 4 4 2 128000 1300 4 3 2 127000 1300 4 3 2 128000 1500 5 3 2 134000 1400 4 3 2 134000 1500 6 3 2 136500 1500 6 3 2 130000 1300 4 3 2 186500 2100 6 5 3 168500 2100 6 5 3 169950 1800 2 3 2 179900 2000 0 4 3 146500 2000 3 4 2 104500 1600 16 3 2 154000 2100 0 4 2.5 110000 1200 14 3 2 100000 1000 13 2 2 155000 1600 13 3 2 161900 1900 12 3 2.5 169000 1700 14 3 2 134000 1700 13 2 2 145000 1900 8 3 2.5 132500 1800 12 3 2.5 131000 1900 10 3 2.5 147000 2100 9 5 3 190000 2100 9 4 2.5 147500 2100 8 3 2.5 175000 2200 8 4 2 188000 2700 8 3 3 172000 2700 7 4 3 163000 1800 8 3 2 209900 2700 8 4 3 163000 1900 8 3 2 179000 2700 8 3 3 149000 1800 8 3 2 121000 1800 3 4 2 134900 1800 5 4 2 160000 1800 8 3 2 164000 2500 1 4 2 148500 1800 3 4 2.5 100000 1600 6 3 2 107000 1600 6 3 2 111000 1600 6 3 2 100000 1600 4 3 2 148000 1600 6 3 2 127500 1800 6 4 3 136500 2000 7 4 2.5 160000 2500 2 4 3 152500 2100 3 4 2.5 132000 1600 3 3 2 126500 1600 3 3 2 149750 1800 7 3 2 165000 2400 7 4 2.5 137500 2000 7 4 2.5 145000 1900 8 4 2 136000 1800 7 3 2 156500 2400 6 4 2.5Explanation / Answer
Using minitab:
The regression equation is
PRICE = 36964 + 70.3 SQFT - 86 AGE - 5733 BEDS + 868 BATHS
Predictor Coef SE Coef T P
Constant 36964 15469 2.39 0.019
SQFT 70.332 8.925 7.88 0.000
AGE -86.3 208.7 -0.41 0.680
BEDS -5733 4615 -1.24 0.217
BATHS 868 8846 0.10 0.922
S = 23441.5 R-Sq = 64.3% R-Sq(adj) = 62.8%
Analysis of Variance
Source DF SS MS F P
Regression 4 93189429032 23297357258 42.40 0.000
Residual Error 94 51653466538 549504963
Total 98 1.44843E+11
Source DF Seq SS
SQFT 1 92329044138
AGE 1 11164928
BEDS 1 843928672
BATHS 1 5291293
Unusual Observations
Obs SQFT PRICE Fit SE Fit Residual St Resid
1 1700 138000 132691 18364 5309 0.36 X
2 2100 105700 162345 4036 -56645 -2.45R
3 700 22000 71368 10705 -49368 -2.37RX
21 3500 330000 263233 9178 66767 3.10RX
22 2400 233900 184565 7389 49335 2.22R
28 2700 149900 206015 4686 -56115 -2.44R
31 2100 219000 167213 5634 51787 2.28R
34 2500 247000 190394 5668 56606 2.49R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Regression Analysis: PRICE versus AGE
The regression equation is
PRICE = 155294 - 157 AGE
Predictor Coef SE Coef T P
Constant 155294 4744 32.73 0.000
AGE -156.8 333.0 -0.47 0.639
S = 38598.2 R-Sq = 0.2% R-Sq(adj) = 0.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 330407690 330407690 0.22 0.639
Residual Error 97 1.44512E+11 1489819463
Total 98 1.44843E+11
Unusual Observations
Obs AGE PRICE Fit SE Fit Residual St Resid
1 97.0 138000 140083 29822 -2083 -0.09 X
3 49.0 22000 147610 14128 -125610 -3.50RX
4 23.0 255000 151687 6271 103313 2.71R
21 0.0 330000 155294 4744 174706 4.56R
22 0.0 233900 155294 4744 78606 2.05R
29 24.0 235000 151530 6536 83470 2.19R
34 24.0 247000 151530 6536 95470 2.51R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI
1 154980 4395 (146258, 163703) (77879, 232082)
Values of Predictors for New Observations
New Obs AGE
1 2.00
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI
1 153726 3925 (145935, 161516) (76724, 230728)
Values of Predictors for New Observations
New Obs AGE
1 10.0
Hope this will be helpful. Thanks and God Bless you ;-)
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